Module_7_Project.ipynb

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      "source": [
        "#Module 7 Project: Movie Recommendation Engine"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Module Coordinator: Nikunj Nawal`"
      ],
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        "id": "nY0Fv5ynQ03G"
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      "source": [
        "In this module, we will try to create a Movie Recommendation System using different unsupervised learning techniques."
      ],
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      "source": [
        "# dataset download\n",
        "\n",
        "# !wget https://files.grouplens.org/datasets/movielens/ml-100k.zip\n",
        "!wget https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\n",
        "!unzip ml-latest-small.zip"
      ],
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          "text": [
            "--2023-04-13 04:35:22--  https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\n",
            "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n",
            "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 978202 (955K) [application/zip]\n",
            "Saving to: ‘ml-latest-small.zip’\n",
            "\n",
            "ml-latest-small.zip 100%[===================>] 955.28K  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-04-13 04:35:22 (8.13 MB/s) - ‘ml-latest-small.zip’ saved [978202/978202]\n",
            "\n",
            "Archive:  ml-latest-small.zip\n",
            "   creating: ml-latest-small/\n",
            "  inflating: ml-latest-small/links.csv  \n",
            "  inflating: ml-latest-small/tags.csv  \n",
            "  inflating: ml-latest-small/ratings.csv  \n",
            "  inflating: ml-latest-small/README.txt  \n",
            "  inflating: ml-latest-small/movies.csv  \n"
          ]
        }
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      "cell_type": "markdown",
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      "source": [
        "## Clustering based recommendation"
      ]
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    {
      "cell_type": "code",
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        "id": "HtuurMa5aeiX"
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      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from scipy.sparse import csr_matrix\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import itertools\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "# Import the Movies dataset\n",
        "movies = pd.read_csv('ml-latest-small/movies.csv')\n",
        "movies.head()"
      ],
      "execution_count": null,
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          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                   Adventure|Children|Fantasy  \n",
              "2                               Comedy|Romance  \n",
              "3                         Comedy|Drama|Romance  \n",
              "4                                       Comedy  "
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        "# Import the ratings dataset\n",
        "ratings = pd.read_csv('ml-latest-small/ratings.csv')\n",
        "ratings.head()"
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      "execution_count": null,
      "outputs": [
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              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
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      "source": [
        "# Print the number of records and the total number of movies\n",
        "print('The dataset contains: ', len(ratings), ' ratings of ', len(movies), ' movies.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dataset contains:  100836  ratings of  9742  movies.\n"
          ]
        }
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      "source": [
        "### Romance versus Science Fiction\n",
        "\n",
        "We will start by considering a subset of users and discovering what are their favourite genre. We will do this by defining a function that will calculate each user’s average rating for all science fiction and romance movies."
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      "source": [
        "# Function to get the genre ratings\n",
        "def get_genre_ratings(ratings, movies, genres, column_names):\n",
        "    genre_ratings = pd.DataFrame()\n",
        "    for genre in genres:        \n",
        "        genre_movies = movies[movies['genres'].str.contains(genre) ]\n",
        "        avg_genre_votes_per_user = ratings[ratings['movieId'].isin(genre_movies['movieId'])].loc[:, ['userId', 'rating']].groupby(['userId'])['rating'].mean().round(2)\n",
        "        \n",
        "        genre_ratings = pd.concat([genre_ratings, avg_genre_votes_per_user], axis=1)\n",
        "        \n",
        "    genre_ratings.columns = column_names\n",
        "    return genre_ratings# Calculate the average rating of romance and scifi movies\n",
        "genre_ratings = get_genre_ratings(ratings, movies, ['Romance', 'Sci-Fi'], ['avg_romance_rating', 'avg_scifi_rating'])\n",
        "genre_ratings.head()"
      ],
      "execution_count": null,
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          "data": {
            "text/plain": [
              "   avg_romance_rating  avg_scifi_rating\n",
              "1                4.31              4.22\n",
              "2                4.50              3.88\n",
              "3                0.50              4.20\n",
              "4                3.38              2.83\n",
              "5                3.09              2.50"
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              "      <th></th>\n",
              "      <th>avg_romance_rating</th>\n",
              "      <th>avg_scifi_rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.31</td>\n",
              "      <td>4.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.50</td>\n",
              "      <td>3.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.50</td>\n",
              "      <td>4.20</td>\n",
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              "      <th>4</th>\n",
              "      <td>3.38</td>\n",
              "      <td>2.83</td>\n",
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              "      <th>5</th>\n",
              "      <td>3.09</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79b5Uz3KcmSu"
      },
      "source": [
        "In order to have a more delimited subset of people to study, we are going to bias our grouping to only get ratings from those users that like either romance or science fiction movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "AdiswAdbceds",
        "outputId": "607d95a5-ea05-4a92-e7c6-e3325498377a"
      },
      "source": [
        "# Function to get the biased dataset\n",
        "def bias_genre_rating_dataset(genre_ratings, score_limit_1, score_limit_2):\n",
        "    biased_dataset =    genre_ratings[((genre_ratings['avg_romance_rating'] < score_limit_1 - 0.2) & (genre_ratings['avg_scifi_rating'] > score_limit_2)) | ((genre_ratings['avg_scifi_rating'] < score_limit_1) & (genre_ratings['avg_romance_rating'] > score_limit_2))]\n",
        "    biased_dataset = pd.concat([biased_dataset[:300], genre_ratings[:2]])\n",
        "    biased_dataset = pd.DataFrame(biased_dataset.to_records())\n",
        "    return biased_dataset\n",
        "\n",
        "# Bias the dataset\n",
        "biased_dataset = bias_genre_rating_dataset(genre_ratings, 3.2, 2.5)\n",
        "\n",
        "# Printing the resulting number of records & the head of the dataset\n",
        "print( \"Number of records: \", len(biased_dataset))\n",
        "biased_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records:  162\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  avg_romance_rating  avg_scifi_rating\n",
              "0      3                0.50              4.20\n",
              "1      4                3.38              2.83\n",
              "2      5                3.09              2.50\n",
              "3      7                2.65              3.15\n",
              "4      9                3.17              3.00"
            ],
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              "      <th>0</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "MLCoVvz0ctAh",
        "outputId": "1140854a-63d6-4309-cc14-1a0a3f686aa3"
      },
      "source": [
        "# Defining the scatterplot drawing function\n",
        "def draw_scatterplot(x_data, x_label, y_data, y_label):\n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    ax = fig.add_subplot(111)    \n",
        "    plt.xlim(0, 5)\n",
        "    plt.ylim(0, 5)\n",
        "    ax.set_xlabel(x_label)\n",
        "    ax.set_ylabel(y_label)\n",
        "    ax.scatter(x_data, y_data, s=30)\n",
        "    \n",
        "# Plot the scatterplot\n",
        "draw_scatterplot(biased_dataset['avg_scifi_rating'],'Avg scifi rating', biased_dataset['avg_romance_rating'], 'Avg romance rating')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "LRfknsM1c1Ur",
        "outputId": "36e47254-eded-4738-ae75-902ab889846d"
      },
      "source": [
        "# Let's turn our dataset into a list\n",
        "X = biased_dataset[['avg_scifi_rating','avg_romance_rating']].values\n",
        "\n",
        "# Import KMeans\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Create an instance of KMeans to find two clusters\n",
        "kmeans_1 = KMeans(n_clusters=2)\n",
        "\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions = kmeans_1.fit_predict(X)\n",
        "\n",
        "# Defining the cluster plotting function\n",
        "def draw_clusters(biased_dataset, predictions, cmap='viridis'):\n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    ax = fig.add_subplot(111)\n",
        "    plt.xlim(0, 5)\n",
        "    plt.ylim(0, 5)\n",
        "    ax.set_xlabel('Avg scifi rating')\n",
        "    ax.set_ylabel('Avg romance rating')\n",
        "    clustered = pd.concat([biased_dataset.reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
        "    plt.scatter(clustered['avg_scifi_rating'], clustered['avg_romance_rating'], c=clustered['group'], s=20, cmap=cmap)\n",
        "    \n",
        "# Plot\n",
        "draw_clusters(biased_dataset, predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "4tVnggzlc-rg",
        "outputId": "e03d57c4-b422-4073-83eb-ad05995c5ffe"
      },
      "source": [
        "# Create an instance of KMeans to find three clusters\n",
        "kmeans_2 = KMeans(n_clusters=3)\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions_2 = kmeans_2.fit_predict(X)\n",
        "# Plot\n",
        "draw_clusters(biased_dataset, predictions_2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xi5ObyC0dLle"
      },
      "source": [
        "It is evident now that the science-fiction rating has started to come into play:\n",
        "\n",
        "    People who like sci-fi and romance modeately belong to the green group.\n",
        "    People who like scifi but not romance belong to the yello group.\n",
        "    People who like romance but not sci-fi belong to the purple group."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5w_-jzld_rM"
      },
      "source": [
        "Finding optimal clusters using silhouette score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HyzcralUdFxj",
        "outputId": "bba76828-a042-4129-b114-776c93bbaa9f"
      },
      "source": [
        "# Selecting our dataset to study\n",
        "df = biased_dataset[['avg_scifi_rating','avg_romance_rating']]\n",
        "\n",
        "# Choose the range of k values to test.\n",
        "# We added a stride of 5 to improve performance. We don't need to calculate the error for every k value\n",
        "possible_k_values = range(2, len(X)+1, 5)\n",
        "\n",
        "# Define function to calculate the clustering errors\n",
        "def clustering_errors(k, data):\n",
        "    kmeans = KMeans(n_clusters=k).fit(data)\n",
        "    predictions = kmeans.predict(data)\n",
        "    #cluster_centers = kmeans.cluster_centers_\n",
        "    # errors = [mean_squared_error(row, cluster_centers[cluster]) for row, cluster in zip(data.values, predictions)]\n",
        "    # return sum(errors)\n",
        "    silhouette_avg = silhouette_score(data, predictions)\n",
        "    return silhouette_avg\n",
        "\n",
        "# Calculate error values for all k values we're interested in\n",
        "errors_per_k = [clustering_errors(k, X) for k in possible_k_values]\n",
        "\n",
        "# Plot the each value of K vs. the silhouette score at that value\n",
        "fig, ax = plt.subplots(figsize=(16, 6))\n",
        "plt.plot(possible_k_values, errors_per_k)\n",
        "\n",
        "# Ticks and grid\n",
        "xticks = np.arange(min(possible_k_values), max(possible_k_values)+1, 5.0)\n",
        "ax.set_xticks(xticks, minor=False)\n",
        "ax.set_xticks(xticks, minor=True)\n",
        "ax.xaxis.grid(True, which='both')\n",
        "yticks = np.arange(round(min(errors_per_k), 2), max(errors_per_k), .05)\n",
        "ax.set_yticks(yticks, minor=False)\n",
        "ax.set_yticks(yticks, minor=True)\n",
        "ax.yaxis.grid(True, which='both')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "<ipython-input-11-ea56d5ac5a15>:10: ConvergenceWarning: Number of distinct clusters (157) found smaller than n_clusters (162). Possibly due to duplicate points in X.\n",
            "  kmeans = KMeans(n_clusters=k).fit(data)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j02Q87YjepyP"
      },
      "source": [
        "We will chose the K = 7 as it is the one that yields the best score and will be easier to visualize.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "lV1yJyAbeWHC",
        "outputId": "5b31eecc-5e04-42c5-830f-4206dfa4a174"
      },
      "source": [
        "# Create an instance of KMeans to find seven clusters\n",
        "kmeans_4 = KMeans(n_clusters=7)\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions_4 = kmeans_4.fit_predict(X)\n",
        "# Plot\n",
        "draw_clusters(biased_dataset, predictions_4, cmap='Accent')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bqqi2h3FfaXs"
      },
      "source": [
        "Up to now, we have only analyzed romance and science-fiction movies. Let us see what happens when adding other genre to our analysis by adding Action movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "Wv4ykXxweyrf",
        "outputId": "fd77535e-0823-4365-adcf-6a1d4eb43969"
      },
      "source": [
        "# Select our biased dataset and add action genre\n",
        "biased_dataset_3_genres = get_genre_ratings(ratings, movies, ['Romance','Sci-Fi', 'Action'],                                          \n",
        "['avg_romance_rating', 'avg_scifi_rating', 'avg_action_rating'])\n",
        "# Drop null values\n",
        "biased_dataset_3_genres = bias_genre_rating_dataset(biased_dataset_3_genres, 3.2, 2.5).dropna()\n",
        "# Print the number of records and the head of our dataset\n",
        "print( \"Number of records: \", len(biased_dataset_3_genres))\n",
        "biased_dataset_3_genres.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records:  161\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  avg_romance_rating  avg_scifi_rating  avg_action_rating\n",
              "0      3                0.50              4.20               3.57\n",
              "1      4                3.38              2.83               3.32\n",
              "2      5                3.09              2.50               3.11\n",
              "3      7                2.65              3.15               3.26\n",
              "4      9                3.17              3.00               3.12"
            ],
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef27aa93-16d2-47d5-a039-009a0b3102a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ef27aa93-16d2-47d5-a039-009a0b3102a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "bmxE7OEbfjgH",
        "outputId": "394a6148-3c94-49e6-cad2-7a1e38f2822c"
      },
      "source": [
        "# Turn dataset into a list\n",
        "X_with_action = biased_dataset_3_genres[['avg_scifi_rating','avg_romance_rating','avg_action_rating']].values# Create an instance of KMeans to find seven clusters\n",
        "kmeans_5 = KMeans(n_clusters=7)\n",
        "# Use fit_predict to cluster the dataset\n",
        "predictions_5 = kmeans_5.fit_predict(X_with_action)\n",
        "# Define 3d plotting function\n",
        "def draw_clusters_3d(biased_dataset_3, predictions):\n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    ax = fig.add_subplot(111)\n",
        "    plt.xlim(0, 5)\n",
        "    plt.ylim(0, 5)\n",
        "    ax.set_xlabel('Avg scifi rating')\n",
        "    ax.set_ylabel('Avg romance rating')\n",
        "    clustered = pd.concat([biased_dataset_3.reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
        "    colors = itertools.cycle(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])\n",
        "    for g in clustered.group.unique():\n",
        "        color = next(colors)\n",
        "        for index, point in clustered[clustered.group == g].iterrows():\n",
        "            if point['avg_action_rating'].astype(float) > 3: \n",
        "                size = 50\n",
        "            else:\n",
        "                size = 15\n",
        "            plt.scatter(point['avg_scifi_rating'], \n",
        "                        point['avg_romance_rating'], \n",
        "                        s=size, \n",
        "                        color=color)# Plot\n",
        "draw_clusters_3d(biased_dataset_3_genres, predictions_5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Upy7G0Ta5MH"
      },
      "source": [
        "The size of the dots represent the ratings of the action movies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzfFJTMJbGCk"
      },
      "source": [
        "### Taking users into consideration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JahmtB_0f5fd"
      },
      "source": [
        "Once we have seen and understood how the K-Means algorithm group the users by their movie genre preferences, we are going to take a bigger picture of the dataset and explore how users rate individual movies.\n",
        "\n",
        "To do so, we will subset the dataset by ‘userid’ vs ‘user rating’ as follows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "oy6yk-kxfuxC",
        "outputId": "ae9cfdae-ba7c-470b-d026-e10e180f492f"
      },
      "source": [
        "# Merge the two tables then pivot so we have Users X Movies dataframe\n",
        "ratings_title = pd.merge(ratings, movies[['movieId', 'title']], on='movieId' )\n",
        "user_movie_ratings = pd.pivot_table(ratings_title, index='userId', columns= 'title', values='rating')\n",
        "\n",
        "# Print he number of dimensions and a subset of the dataset\n",
        "print('dataset dimensions: ', user_movie_ratings.shape, '\\n\\nSubset example:')\n",
        "user_movie_ratings.iloc[:6, :10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataset dimensions:  (610, 9719) \n",
            "\n",
            "Subset example:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
              "userId                                                        \n",
              "1              NaN                                      NaN   \n",
              "2              NaN                                      NaN   \n",
              "3              NaN                                      NaN   \n",
              "4              NaN                                      NaN   \n",
              "5              NaN                                      NaN   \n",
              "6              NaN                                      NaN   \n",
              "\n",
              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
              "userId                                                \n",
              "1                          NaN                  NaN   \n",
              "2                          NaN                  NaN   \n",
              "3                          NaN                  NaN   \n",
              "4                          NaN                  NaN   \n",
              "5                          NaN                  NaN   \n",
              "6                          NaN                  NaN   \n",
              "\n",
              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
              "userId                                                               \n",
              "1                             NaN                              NaN   \n",
              "2                             NaN                              NaN   \n",
              "3                             NaN                              NaN   \n",
              "4                             NaN                              NaN   \n",
              "5                             NaN                              NaN   \n",
              "6                             NaN                              NaN   \n",
              "\n",
              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
              "userId                                                                          \n",
              "1                      NaN                   NaN                          NaN   \n",
              "2                      NaN                   NaN                          NaN   \n",
              "3                      NaN                   NaN                          NaN   \n",
              "4                      NaN                   NaN                          NaN   \n",
              "5                      NaN                   NaN                          NaN   \n",
              "6                      NaN                   NaN                          NaN   \n",
              "\n",
              "title   *batteries not included (1987)  \n",
              "userId                                  \n",
              "1                                  NaN  \n",
              "2                                  NaN  \n",
              "3                                  NaN  \n",
              "4                                  NaN  \n",
              "5                                  NaN  \n",
              "6                                  NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf50ab72-670e-4b27-ad18-bbf3fe4bbe48\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>title</th>\n",
              "      <th>'71 (2014)</th>\n",
              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
              "      <th>'Round Midnight (1986)</th>\n",
              "      <th>'Salem's Lot (2004)</th>\n",
              "      <th>'Til There Was You (1997)</th>\n",
              "      <th>'Tis the Season for Love (2015)</th>\n",
              "      <th>'burbs, The (1989)</th>\n",
              "      <th>'night Mother (1986)</th>\n",
              "      <th>(500) Days of Summer (2009)</th>\n",
              "      <th>*batteries not included (1987)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf50ab72-670e-4b27-ad18-bbf3fe4bbe48')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-bf50ab72-670e-4b27-ad18-bbf3fe4bbe48');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSB8eHPqf-Xc",
        "outputId": "7d184f76-1aff-468b-ad91-d518d599ddec"
      },
      "source": [
        "# Define Function to get the most rated movies\n",
        "def get_most_rated_movies(user_movie_ratings, max_number_of_movies):\n",
        "    # 1- Count\n",
        "    user_movie_ratings = user_movie_ratings.append(user_movie_ratings.count(), ignore_index=True)\n",
        "    # 2- sort\n",
        "    user_movie_ratings_sorted = user_movie_ratings.sort_values(len(user_movie_ratings)-1, axis=1, ascending=False)\n",
        "    user_movie_ratings_sorted = user_movie_ratings_sorted.drop(user_movie_ratings_sorted.tail(1).index)\n",
        "    # 3- slice\n",
        "    most_rated_movies = user_movie_ratings_sorted.iloc[:, :max_number_of_movies]\n",
        "    return most_rated_movies\n",
        "\n",
        "# Define function to get the user who rate a movie the most\n",
        "def get_users_who_rate_the_most(most_rated_movies, n_users):\n",
        "    most_rated_movies['num_ratings'] = - most_rated_movies.isnull().sum(axis=1)\n",
        "    most_rated_movies = most_rated_movies.sort_values(by=['num_ratings'], ascending=False)\n",
        "    most_rated_movies = most_rated_movies.iloc[:n_users, :]\n",
        "    most_rated_movies = most_rated_movies.drop('num_ratings', axis=1)\n",
        "    return most_rated_movies\n",
        "\n",
        "# Define the sorting by rating function\n",
        "def sort_by_rating_density(user_movie_ratings, n_movies, n_users):\n",
        "    most_rated_movies = get_most_rated_movies(user_movie_ratings, n_movies)\n",
        "    most_rated_movies = get_users_who_rate_the_most(most_rated_movies, n_users)\n",
        "    return most_rated_movies\n",
        "    \n",
        "# choose the number of movies and users and sort\n",
        "n_movies = 30\n",
        "n_users = 18\n",
        "most_rated_movies_users_selection = sort_by_rating_density(user_movie_ratings, n_movies, n_users)\n",
        "\n",
        "# Print the result\n",
        "# print(most_rated_movies_users_selection)\n",
        "print('dataset dimensions: ', most_rated_movies_users_selection.shape)\n",
        "print(most_rated_movies_users_selection.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataset dimensions:  (18, 30)\n",
            "title  Forrest Gump (1994)  Shawshank Redemption, The (1994)  \\\n",
            "413                    5.0                               5.0   \n",
            "589                    5.0                               4.5   \n",
            "473                    3.0                               5.0   \n",
            "479                    5.0                               5.0   \n",
            "67                     3.5                               3.0   \n",
            "\n",
            "title  Pulp Fiction (1994)  Silence of the Lambs, The (1991)  \\\n",
            "413                    5.0                               4.0   \n",
            "589                    4.5                               3.5   \n",
            "473                    4.0                               4.5   \n",
            "479                    4.0                               4.5   \n",
            "67                     2.0                               3.5   \n",
            "\n",
            "title  Matrix, The (1999)  Star Wars: Episode IV - A New Hope (1977)  \\\n",
            "413                   5.0                                        5.0   \n",
            "589                   4.0                                        5.0   \n",
            "473                   4.5                                        4.0   \n",
            "479                   5.0                                        4.5   \n",
            "67                    4.5                                        5.0   \n",
            "\n",
            "title  Jurassic Park (1993)  Braveheart (1995)  \\\n",
            "413                     4.0                5.0   \n",
            "589                     4.0                4.0   \n",
            "473                     4.5                3.0   \n",
            "479                     5.0                5.0   \n",
            "67                      3.5                2.5   \n",
            "\n",
            "title  Terminator 2: Judgment Day (1991)  Schindler's List (1993)  ...  \\\n",
            "413                                  5.0                      4.0  ...   \n",
            "589                                  4.5                      5.0  ...   \n",
            "473                                  4.0                      5.0  ...   \n",
            "479                                  4.5                      5.0  ...   \n",
            "67                                   3.5                      4.0  ...   \n",
            "\n",
            "title  Star Wars: Episode VI - Return of the Jedi (1983)  \\\n",
            "413                                                  5.0   \n",
            "589                                                  4.5   \n",
            "473                                                  4.0   \n",
            "479                                                  3.5   \n",
            "67                                                   5.0   \n",
            "\n",
            "title  Godfather, The (1972)  Fugitive, The (1993)  Batman (1989)  \\\n",
            "413                      5.0                   5.0            4.0   \n",
            "589                      5.0                   4.0            3.5   \n",
            "473                      5.0                   5.0            4.0   \n",
            "479                      5.0                   3.5            4.5   \n",
            "67                       4.0                   4.5            4.0   \n",
            "\n",
            "title  Saving Private Ryan (1998)  \\\n",
            "413                           5.0   \n",
            "589                           4.0   \n",
            "473                           3.0   \n",
            "479                           4.5   \n",
            "67                            4.0   \n",
            "\n",
            "title  Lord of the Rings: The Two Towers, The (2002)  \\\n",
            "413                                              5.0   \n",
            "589                                              5.0   \n",
            "473                                              5.0   \n",
            "479                                              4.5   \n",
            "67                                               4.0   \n",
            "\n",
            "title  Lord of the Rings: The Return of the King, The (2003)  Aladdin (1992)  \\\n",
            "413                                                  4.0                 4.0   \n",
            "589                                                  4.5                 4.0   \n",
            "473                                                  5.0                 4.0   \n",
            "479                                                  4.0                 4.0   \n",
            "67                                                   4.5                 3.5   \n",
            "\n",
            "title  Fargo (1996)  Sixth Sense, The (1999)  \n",
            "413             5.0                      3.0  \n",
            "589             4.0                      3.5  \n",
            "473             4.0                      5.0  \n",
            "479             4.0                      4.0  \n",
            "67              2.5                      2.5  \n",
            "\n",
            "[5 rows x 30 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-0640cec50cc9>:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  user_movie_ratings = user_movie_ratings.append(user_movie_ratings.count(), ignore_index=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMmuIqVxgGAN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "outputId": "1fd5d467-7a40-4e72-c362-d24a3f0ef827"
      },
      "source": [
        "# Define the plotting heatmap function\n",
        "def draw_movies_heatmap(most_rated_movies_users_selection, axis_labels=True):\n",
        "    \n",
        "    fig = plt.figure(figsize=(15,4))\n",
        "    ax = plt.gca()\n",
        "    \n",
        "    # Draw heatmap\n",
        "    heatmap = ax.imshow(most_rated_movies_users_selection,  interpolation='nearest', vmin=0, vmax=5, aspect='auto')\n",
        "    if axis_labels:\n",
        "        ax.set_yticks(np.arange(most_rated_movies_users_selection.shape[0]) , minor=False)\n",
        "        ax.set_xticks(np.arange(most_rated_movies_users_selection.shape[1]) , minor=False)\n",
        "        ax.invert_yaxis()\n",
        "        ax.xaxis.tick_top()\n",
        "        labels = most_rated_movies_users_selection.columns.str[:40]\n",
        "        ax.set_xticklabels(labels, minor=False)\n",
        "        ax.set_yticklabels(most_rated_movies_users_selection.index, minor=False)\n",
        "        plt.setp(ax.get_xticklabels(), rotation=90)\n",
        "    else:\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "    \n",
        "    ax.grid(False)\n",
        "    ax.set_ylabel('User id')# Separate heatmap from color bar\n",
        "    divider = make_axes_locatable(ax)\n",
        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)# Color bar\n",
        "    cbar = fig.colorbar(heatmap, ticks=[5, 4, 3, 2, 1, 0], cax=cax)\n",
        "    cbar.ax.set_yticklabels(['5 stars', '4 stars','3 stars','2 stars','1 stars','0 stars'])\n",
        "    plt.show()# Print the heatmap\n",
        "draw_movies_heatmap(most_rated_movies_users_selection)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTdEC1i5hS-M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6846e6f-00e8-48de-adf7-25c54537ee0d"
      },
      "source": [
        "# Pivot the dataset and choose the first 1000 movies\n",
        "user_movie_ratings =  pd.pivot_table(ratings_title, index='userId', columns= 'title', values='rating')\n",
        "most_rated_movies_1k = get_most_rated_movies(user_movie_ratings, 1000).replace(np.nan, 0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-0640cec50cc9>:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  user_movie_ratings = user_movie_ratings.append(user_movie_ratings.count(), ignore_index=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMGpCM8SikyB"
      },
      "source": [
        "from scipy import sparse\n",
        "\n",
        "# Conversion to sparse csr matrix\n",
        "sparse_ratings = sparse.csr_matrix(most_rated_movies_1k.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5j6M6TL7j5B7"
      },
      "source": [
        "### Large Scale Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcvHmGnZipva",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d4bb6ce-7875-47de-9241-08f7143ae22b"
      },
      "source": [
        "# 20 clusters\n",
        "predictions = KMeans(n_clusters=20, algorithm='full').fit_predict(sparse_ratings)\n",
        "# Select the mas number of users and movies heatmap cluster\n",
        "# Cluster and print some of them\n",
        "clustered = pd.concat([get_most_rated_movies(user_movie_ratings, 1000).reset_index(), pd.DataFrame({'group':predictions})], axis=1)\n",
        "# draw_movie_clusters(clustered, max_users, max_movies)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:1366: FutureWarning: algorithm='full' is deprecated, it will be removed in 1.3. Using 'lloyd' instead.\n",
            "  warnings.warn(\n",
            "<ipython-input-16-0640cec50cc9>:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  user_movie_ratings = user_movie_ratings.append(user_movie_ratings.count(), ignore_index=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wr82U3hYo7gg"
      },
      "source": [
        "### Predictions and Recommendation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KSx-kAa2j9rJ",
        "outputId": "6911b487-c73f-414b-ab50-8a7a7d69406d"
      },
      "source": [
        "# Pick a cluster ID from the clusters above\n",
        "cluster_number = 11\n",
        "# Let's filter to only see the region of the dataset with the most number of values \n",
        "n_users = 75\n",
        "n_movies = 300\n",
        "cluster = clustered[clustered.group == cluster_number].drop(['index', 'group'], axis=1)\n",
        "print(cluster)\n",
        "# Sort and print the cluster\n",
        "cluster = sort_by_rating_density(cluster, n_movies, n_users)\n",
        "draw_movies_heatmap(cluster, axis_labels=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Forrest Gump (1994)  Shawshank Redemption, The (1994)  \\\n",
            "17                   4.5                               5.0   \n",
            "61                   5.0                               4.0   \n",
            "62                   3.5                               5.0   \n",
            "102                  4.0                               5.0   \n",
            "131                  4.0                               4.5   \n",
            "140                  4.0                               4.0   \n",
            "165                  4.0                               4.5   \n",
            "176                  5.0                               5.0   \n",
            "218                  NaN                               5.0   \n",
            "219                  5.0                               5.0   \n",
            "231                  4.0                               5.0   \n",
            "238                  5.0                               5.0   \n",
            "281                  4.5                               4.5   \n",
            "327                  3.0                               5.0   \n",
            "331                  4.5                               4.5   \n",
            "338                  4.0                               4.0   \n",
            "355                  4.0                               4.5   \n",
            "356                  3.5                               4.0   \n",
            "380                  4.0                               3.5   \n",
            "433                  4.5                               4.0   \n",
            "488                  4.0                               NaN   \n",
            "524                  4.0                               4.5   \n",
            "559                  3.5                               NaN   \n",
            "560                  4.0                               5.0   \n",
            "572                  4.5                               4.5   \n",
            "579                  4.0                               5.0   \n",
            "\n",
            "     Pulp Fiction (1994)  Silence of the Lambs, The (1991)  \\\n",
            "17                   4.0                               4.5   \n",
            "61                   4.5                               5.0   \n",
            "62                   5.0                               NaN   \n",
            "102                  5.0                               5.0   \n",
            "131                  4.0                               3.5   \n",
            "140                  4.0                               NaN   \n",
            "165                  4.5                               4.0   \n",
            "176                  5.0                               5.0   \n",
            "218                  4.0                               4.5   \n",
            "219                  5.0                               NaN   \n",
            "231                  5.0                               NaN   \n",
            "238                  5.0                               3.5   \n",
            "281                  4.0                               4.0   \n",
            "327                  5.0                               NaN   \n",
            "331                  4.0                               4.5   \n",
            "338                  2.5                               5.0   \n",
            "355                  NaN                               NaN   \n",
            "356                  4.5                               4.5   \n",
            "380                  NaN                               NaN   \n",
            "433                  5.0                               4.0   \n",
            "488                  4.0                               4.0   \n",
            "524                  NaN                               3.5   \n",
            "559                  4.0                               4.0   \n",
            "560                  4.5                               4.0   \n",
            "572                  5.0                               4.5   \n",
            "579                  5.0                               4.5   \n",
            "\n",
            "     Matrix, The (1999)  Star Wars: Episode IV - A New Hope (1977)  \\\n",
            "17                  4.5                                        4.0   \n",
            "61                  5.0                                        4.5   \n",
            "62                  3.5                                        5.0   \n",
            "102                 4.0                                        4.0   \n",
            "131                 4.5                                        2.0   \n",
            "140                 3.5                                        4.0   \n",
            "165                 4.0                                        4.5   \n",
            "176                 3.0                                        2.5   \n",
            "218                 5.0                                        5.0   \n",
            "219                 5.0                                        5.0   \n",
            "231                 4.5                                        4.5   \n",
            "238                 5.0                                        5.0   \n",
            "281                 5.0                                        4.0   \n",
            "327                 2.0                                        5.0   \n",
            "331                 5.0                                        NaN   \n",
            "338                 NaN                                        NaN   \n",
            "355                 4.0                                        NaN   \n",
            "356                 NaN                                        4.0   \n",
            "380                 5.0                                        3.5   \n",
            "433                 5.0                                        5.0   \n",
            "488                 4.0                                        NaN   \n",
            "524                 4.0                                        4.0   \n",
            "559                 4.5                                        4.0   \n",
            "560                 4.0                                        4.5   \n",
            "572                 5.0                                        5.0   \n",
            "579                 5.0                                        4.0   \n",
            "\n",
            "     Jurassic Park (1993)  Braveheart (1995)  \\\n",
            "17                    3.5                4.5   \n",
            "61                    NaN                4.5   \n",
            "62                    2.5                2.5   \n",
            "102                   4.0                4.5   \n",
            "131                   NaN                NaN   \n",
            "140                   3.0                3.5   \n",
            "165                   NaN                NaN   \n",
            "176                   3.5                NaN   \n",
            "218                   4.0                NaN   \n",
            "219                   5.0                NaN   \n",
            "231                   3.0                4.5   \n",
            "238                   4.0                4.5   \n",
            "281                   4.0                NaN   \n",
            "327                   2.0                1.0   \n",
            "331                   3.5                3.5   \n",
            "338                   4.0                NaN   \n",
            "355                   NaN                NaN   \n",
            "356                   4.0                4.0   \n",
            "380                   4.0                NaN   \n",
            "433                   5.0                4.5   \n",
            "488                   3.5                4.5   \n",
            "524                   4.0                NaN   \n",
            "559                   NaN                4.0   \n",
            "560                   4.5                5.0   \n",
            "572                   5.0                5.0   \n",
            "579                   4.0                4.5   \n",
            "\n",
            "     Terminator 2: Judgment Day (1991)  Schindler's List (1993)  ...  \\\n",
            "17                                 4.5                      4.5  ...   \n",
            "61                                 NaN                      4.5  ...   \n",
            "62                                 4.0                      4.0  ...   \n",
            "102                                NaN                      3.5  ...   \n",
            "131                                NaN                      NaN  ...   \n",
            "140                                NaN                      NaN  ...   \n",
            "165                                NaN                      4.5  ...   \n",
            "176                                NaN                      4.5  ...   \n",
            "218                                4.5                      NaN  ...   \n",
            "219                                5.0                      NaN  ...   \n",
            "231                                4.0                      4.5  ...   \n",
            "238                                3.5                      NaN  ...   \n",
            "281                                4.5                      5.0  ...   \n",
            "327                                1.0                      5.0  ...   \n",
            "331                                3.5                      4.0  ...   \n",
            "338                                NaN                      5.0  ...   \n",
            "355                                NaN                      NaN  ...   \n",
            "356                                3.5                      5.0  ...   \n",
            "380                                2.5                      NaN  ...   \n",
            "433                                4.5                      5.0  ...   \n",
            "488                                3.5                      NaN  ...   \n",
            "524                                NaN                      4.0  ...   \n",
            "559                                3.0                      NaN  ...   \n",
            "560                                4.0                      4.0  ...   \n",
            "572                                5.0                      NaN  ...   \n",
            "579                                4.0                      3.0  ...   \n",
            "\n",
            "     Scary Movie 2 (2001)  About Schmidt (2002)  21 Jump Street (2012)  \\\n",
            "17                    NaN                   NaN                    NaN   \n",
            "61                    NaN                   NaN                    NaN   \n",
            "62                    NaN                   1.0                    NaN   \n",
            "102                   3.0                   NaN                    NaN   \n",
            "131                   NaN                   NaN                    NaN   \n",
            "140                   NaN                   NaN                    NaN   \n",
            "165                   NaN                   NaN                    NaN   \n",
            "176                   2.5                   NaN                    NaN   \n",
            "218                   0.5                   NaN                    NaN   \n",
            "219                   NaN                   NaN                    NaN   \n",
            "231                   3.0                   NaN                    NaN   \n",
            "238                   NaN                   NaN                    NaN   \n",
            "281                   NaN                   NaN                    NaN   \n",
            "327                   NaN                   NaN                    NaN   \n",
            "331                   NaN                   NaN                    NaN   \n",
            "338                   NaN                   NaN                    NaN   \n",
            "355                   NaN                   NaN                    NaN   \n",
            "356                   NaN                   NaN                    NaN   \n",
            "380                   NaN                   NaN                    NaN   \n",
            "433                   NaN                   NaN                    NaN   \n",
            "488                   NaN                   4.0                    NaN   \n",
            "524                   NaN                   NaN                    4.0   \n",
            "559                   NaN                   NaN                    3.0   \n",
            "560                   NaN                   3.5                    NaN   \n",
            "572                   4.0                   NaN                    NaN   \n",
            "579                   NaN                   NaN                    NaN   \n",
            "\n",
            "     Cider House Rules, The (1999)  Death Proof (2007)  Moonraker (1979)  \\\n",
            "17                             NaN                 NaN               NaN   \n",
            "61                             NaN                 NaN               NaN   \n",
            "62                             NaN                 NaN               NaN   \n",
            "102                            NaN                 NaN               NaN   \n",
            "131                            3.0                 NaN               NaN   \n",
            "140                            NaN                 NaN               NaN   \n",
            "165                            NaN                 NaN               NaN   \n",
            "176                            NaN                 NaN               NaN   \n",
            "218                            NaN                 4.0               NaN   \n",
            "219                            NaN                 NaN               NaN   \n",
            "231                            NaN                 3.5               NaN   \n",
            "238                            NaN                 NaN               NaN   \n",
            "281                            NaN                 NaN               NaN   \n",
            "327                            NaN                 NaN               NaN   \n",
            "331                            NaN                 NaN               NaN   \n",
            "338                            NaN                 NaN               NaN   \n",
            "355                            NaN                 NaN               NaN   \n",
            "356                            NaN                 NaN               NaN   \n",
            "380                            NaN                 NaN               NaN   \n",
            "433                            NaN                 NaN               NaN   \n",
            "488                            4.5                 NaN               3.0   \n",
            "524                            NaN                 NaN               NaN   \n",
            "559                            NaN                 4.0               NaN   \n",
            "560                            NaN                 NaN               NaN   \n",
            "572                            NaN                 NaN               NaN   \n",
            "579                            NaN                 NaN               NaN   \n",
            "\n",
            "     Virgin Suicides, The (1999)  ¡Three Amigos! (1986)  \\\n",
            "17                           NaN                    NaN   \n",
            "61                           NaN                    NaN   \n",
            "62                           NaN                    NaN   \n",
            "102                          NaN                    NaN   \n",
            "131                          4.0                    NaN   \n",
            "140                          NaN                    3.5   \n",
            "165                          NaN                    NaN   \n",
            "176                          4.0                    NaN   \n",
            "218                          NaN                    NaN   \n",
            "219                          NaN                    NaN   \n",
            "231                          NaN                    NaN   \n",
            "238                          NaN                    3.0   \n",
            "281                          NaN                    4.0   \n",
            "327                          NaN                    NaN   \n",
            "331                          NaN                    NaN   \n",
            "338                          NaN                    NaN   \n",
            "355                          NaN                    NaN   \n",
            "356                          4.0                    NaN   \n",
            "380                          NaN                    NaN   \n",
            "433                          NaN                    NaN   \n",
            "488                          3.0                    NaN   \n",
            "524                          4.5                    NaN   \n",
            "559                          NaN                    NaN   \n",
            "560                          NaN                    4.0   \n",
            "572                          NaN                    NaN   \n",
            "579                          5.0                    NaN   \n",
            "\n",
            "     Waking Ned Devine (a.k.a. Waking Ned) (1998)  Jacob's Ladder (1990)  \n",
            "17                                            NaN                    NaN  \n",
            "61                                            NaN                    NaN  \n",
            "62                                            NaN                    NaN  \n",
            "102                                           NaN                    NaN  \n",
            "131                                           NaN                    NaN  \n",
            "140                                           NaN                    NaN  \n",
            "165                                           NaN                    NaN  \n",
            "176                                           NaN                    NaN  \n",
            "218                                           NaN                    NaN  \n",
            "219                                           3.5                    NaN  \n",
            "231                                           NaN                    NaN  \n",
            "238                                           NaN                    NaN  \n",
            "281                                           NaN                    NaN  \n",
            "327                                           NaN                    NaN  \n",
            "331                                           NaN                    NaN  \n",
            "338                                           NaN                    NaN  \n",
            "355                                           4.0                    NaN  \n",
            "356                                           NaN                    NaN  \n",
            "380                                           NaN                    NaN  \n",
            "433                                           NaN                    NaN  \n",
            "488                                           NaN                    NaN  \n",
            "524                                           NaN                    NaN  \n",
            "559                                           NaN                    NaN  \n",
            "560                                           NaN                    NaN  \n",
            "572                                           NaN                    NaN  \n",
            "579                                           NaN                    NaN  \n",
            "\n",
            "[26 rows x 1000 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-0640cec50cc9>:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  user_movie_ratings = user_movie_ratings.append(user_movie_ratings.count(), ignore_index=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1H3vAespxSf",
        "outputId": "faadc7ba-9b4c-4c59-b084-96e24660b0ff"
      },
      "source": [
        "# Fill in the name of the column/movie. e.g. 'Forrest Gump (1994)'\n",
        "movie_name = \"Matrix, The (1999)\"\n",
        "cluster[movie_name].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.333333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eaBbILNpsWA",
        "outputId": "c0084433-f330-4d5b-c727-f6f871601b98"
      },
      "source": [
        "# The average rating of 20 movies as rated by the users in the cluster\n",
        "cluster.mean().head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fight Club (1999)                                                                 4.480769\n",
              "Forrest Gump (1994)                                                               4.160000\n",
              "Batman Begins (2005)                                                              4.000000\n",
              "Matrix, The (1999)                                                                4.333333\n",
              "Shawshank Redemption, The (1994)                                                  4.583333\n",
              "Shrek (2001)                                                                      3.854167\n",
              "Lord of the Rings: The Return of the King, The (2003)                             4.145833\n",
              "Lord of the Rings: The Two Towers, The (2002)                                     4.000000\n",
              "Truman Show, The (1998)                                                           3.847826\n",
              "Incredibles, The (2004)                                                           4.086957\n",
              "Indiana Jones and the Last Crusade (1989)                                         4.065217\n",
              "Dark Knight, The (2008)                                                           4.478261\n",
              "Pirates of the Caribbean: The Curse of the Black Pearl (2003)                     4.000000\n",
              "Pulp Fiction (1994)                                                               4.456522\n",
              "Toy Story (1995)                                                                  4.065217\n",
              "Kill Bill: Vol. 2 (2004)                                                          3.977273\n",
              "Seven (a.k.a. Se7en) (1995)                                                       4.022727\n",
              "Departed, The (2006)                                                              4.318182\n",
              "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)    4.295455\n",
              "Star Wars: Episode VI - Return of the Jedi (1983)                                 4.136364\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VKa3_ZiyeGg",
        "outputId": "6c767c54-1130-483b-9a61-db5a5604dea7"
      },
      "source": [
        "# Predict for a specific user\n",
        "\n",
        "# Pick a user ID from the dataset\n",
        "user_id = 19\n",
        "# Get all this user's ratings\n",
        "user_2_ratings  = cluster.loc[user_id, :]\n",
        "# Which movies did they not rate? \n",
        "user_2_unrated_movies =  user_2_ratings[user_2_ratings.isnull()]\n",
        "# What are the ratings of these movies the user did not rate?\n",
        "avg_ratings = pd.concat([user_2_unrated_movies, cluster.mean()], axis=1, join='inner').loc[:,0]\n",
        "# Let's sort by rating so the highest rated movies are presented first\n",
        "avg_ratings.sort_values(ascending=False)[:20]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Usual Suspects, The (1995)                                                        4.500000\n",
              "Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)                              4.500000\n",
              "Monty Python's Life of Brian (1979)                                               4.500000\n",
              "Godfather: Part II, The (1974)                                                    4.366667\n",
              "Mary Poppins (1964)                                                               4.300000\n",
              "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)    4.295455\n",
              "The Machinist (2004)                                                              4.250000\n",
              "Toy Story 3 (2010)                                                                4.214286\n",
              "Requiem for a Dream (2000)                                                        4.205882\n",
              "Rain Man (1988)                                                                   4.166667\n",
              "Bourne Ultimatum, The (2007)                                                      4.166667\n",
              "Boondock Saints, The (2000)                                                       4.166667\n",
              "Inception (2010)                                                                  4.147059\n",
              "Watchmen (2009)                                                                   4.050000\n",
              "Others, The (2001)                                                                4.050000\n",
              "Kiss Kiss Bang Bang (2005)                                                        4.050000\n",
              "Equilibrium (2002)                                                                4.041667\n",
              "Pirates of the Caribbean: The Curse of the Black Pearl (2003)                     4.000000\n",
              "Thank You for Smoking (2006)                                                      4.000000\n",
              "Superbad (2007)                                                                   4.000000\n",
              "Name: 0, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4SISKyey5rb"
      },
      "source": [
        "## Using Collaborative Filtering (With fast.ai)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eh_wLcmh4nFi"
      },
      "source": [
        "! [ -e /content ] && pip install -Uqq fastai"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GQhuh1QzBO-"
      },
      "source": [
        "from fastai.tabular.all import *\n",
        "from fastai.collab import *"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_C6VqNfy5KGG"
      },
      "source": [
        "### Training a model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVnoQsA25KGH"
      },
      "source": [
        "For this, we will use the [Movielens 100k data dataset](https://grouplens.org/datasets/movielens/100k/). We can download it easily and decompress it with the following function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSpPNWVo5KGI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "40549789-959e-42b3-ac80-73fad1f2a5a0"
      },
      "source": [
        "path = untar_data(URLs.ML_100k)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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      "source": [
        "The main table is in `u.data`. Since it's not a proper csv, we have to specify a few things while opening it: the tab delimiter, the columns we want to keep and their names."
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "f0ca5f7f-8826-4e92-d577-dc61bca7399f"
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      "source": [
        "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
        "                      usecols=(0,1,2), names=['user','movie','rating'])\n",
        "ratings.head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user  movie  rating\n",
              "0   196    242       3\n",
              "1   186    302       3\n",
              "2    22    377       1\n",
              "3   244     51       2\n",
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      "cell_type": "markdown",
      "metadata": {
        "id": "g0FSMvM05KGP"
      },
      "source": [
        "Movie ids are not ideal to look at things, so we load the corresponding movie id to the title that is in the table `u.item`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3y_V-da5KGQ",
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          "height": 206
        },
        "outputId": "9a0f0523-d97e-4f48-ee72-2300b7194bc5"
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      "source": [
        "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1',\n",
        "                     usecols=(0,1), names=('movie','title'), header=None)\n",
        "movies.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movie              title\n",
              "0      1   Toy Story (1995)\n",
              "1      2   GoldenEye (1995)\n",
              "2      3  Four Rooms (1995)\n",
              "3      4  Get Shorty (1995)\n",
              "4      5     Copycat (1995)"
            ],
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
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      "cell_type": "markdown",
      "metadata": {
        "id": "g11IQqmd5KGR"
      },
      "source": [
        "Next we merge it to our ratings table:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZmPTq285KGT",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "417b4546-aca9-4a62-a8df-7847208aa409"
      },
      "source": [
        "ratings = ratings.merge(movies)\n",
        "ratings.head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user  movie  rating         title\n",
              "0   196    242       3  Kolya (1996)\n",
              "1    63    242       3  Kolya (1996)\n",
              "2   226    242       5  Kolya (1996)\n",
              "3   154    242       3  Kolya (1996)\n",
              "4   306    242       5  Kolya (1996)"
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc90c386-46df-402f-96f3-d9bcf8a88b13 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc90c386-46df-402f-96f3-d9bcf8a88b13');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PksImdkw5KGV"
      },
      "source": [
        "We can then build a `DataLoaders` object from this table. By default, it takes the first column for user, the second column for the item (here our movies) and the third column for the ratings. We need to change the value of `item_name` in our case, to use the titles instead of the ids:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OV-H74uh5KGV"
      },
      "source": [
        "dls = CollabDataLoaders.from_df(ratings, item_name='title', bs=64)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyK3Sy9L5KGW"
      },
      "source": [
        "In all applications, when the data has been assembled in a `DataLoaders`, you can have a look at it with the `show_batch` method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U07N7Tue5KGW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "25dffc11-478d-4aef-858e-335f97076bcd"
      },
      "source": [
        "dls.show_batch()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user</th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>730</td>\n",
              "      <td>Leaving Las Vegas (1995)</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>618</td>\n",
              "      <td>Four Weddings and a Funeral (1994)</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>616</td>\n",
              "      <td>Scream 2 (1997)</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>414</td>\n",
              "      <td>Contact (1997)</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>682</td>\n",
              "      <td>Mr. Holland's Opus (1995)</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>711</td>\n",
              "      <td>Mary Poppins (1964)</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>190</td>\n",
              "      <td>Substitute, The (1996)</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>18</td>\n",
              "      <td>Priest (1994)</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>707</td>\n",
              "      <td>Breaking the Waves (1996)</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>618</td>\n",
              "      <td>Sound of Music, The (1965)</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cMiAsrvW5KGY"
      },
      "source": [
        "fastai can create and train a collaborative filtering model by using `collab_learner`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rXePHqS5KGY"
      },
      "source": [
        "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58blYToq5KGY"
      },
      "source": [
        "It uses a simple dot product model with 50 latent factors. To train it using the 1cycle policy, we just run this command:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ze2sG7Pd5KGZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b01b8421-3ba2-454f-a462-bad0179cfe51"
      },
      "source": [
        "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.964642</td>\n",
              "      <td>0.934397</td>\n",
              "      <td>00:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.877041</td>\n",
              "      <td>0.867244</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.717953</td>\n",
              "      <td>0.823582</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.586626</td>\n",
              "      <td>0.813398</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.487519</td>\n",
              "      <td>0.813816</td>\n",
              "      <td>00:13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXpc4wrh5KGZ"
      },
      "source": [
        "Here's [some benchmarks](https://www.librec.net/release/v1.3/example.html) on the same dataset for the popular Librec system for collaborative filtering. They show best results based on RMSE of 0.91 (scroll down to the 100k dataset), which corresponds to an MSE of `0.91**2 = 0.83`. So in less than a minute, we got pretty good results!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNlR8PdV5KGa"
      },
      "source": [
        "### Interpretation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Kp8v39y5KGa"
      },
      "source": [
        "Let's analyze the results of our previous model. We will keep the 1000 most rated movies for this:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOBM1Dmn5KGa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8aa58bb1-824a-4013-fcbf-27bf4398c368"
      },
      "source": [
        "g = ratings.groupby('title')['rating'].count()\n",
        "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
        "top_movies[:10]"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Star Wars (1977)', 'Contact (1997)', 'Fargo (1996)',\n",
              "       'Return of the Jedi (1983)', 'Liar Liar (1997)',\n",
              "       'English Patient, The (1996)', 'Scream (1996)', 'Toy Story (1995)',\n",
              "       'Air Force One (1997)', 'Independence Day (ID4) (1996)'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iT0TUZeV5KGa"
      },
      "source": [
        "### Movie bias"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTfd24Ce5KGb"
      },
      "source": [
        "Our model has learned one bias per movie, a unique number independent of users that can be interpreted as the intrinsic \"value\" of the movie. We can grab the bias of each movie in our `top_movies` list with the following command:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6_3vygf5KGb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "faf822e1-7c58-47f4-b570-733cfec8d08d"
      },
      "source": [
        "movie_bias = learn.model.bias(top_movies, is_item=True)\n",
        "movie_bias.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LWSBSXB5KGb"
      },
      "source": [
        "Let's compare those biases with the average ratings:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-cmUxrz5KGc"
      },
      "source": [
        "mean_ratings = ratings.groupby('title')['rating'].mean()\n",
        "movie_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_movies,movie_bias)]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQ9NpRIY5KGd"
      },
      "source": [
        "Now let's have a look at the movies with the worst bias:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPK_3uoL5KGe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "217ee5f1-ba57-46e3-fe2c-62538c5a5792"
      },
      "source": [
        "item0 = lambda o:o[0]\n",
        "sorted(movie_ratings, key=item0)[:15]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.3278),\n",
              "  'Children of the Corn: The Gathering (1996)',\n",
              "  1.3157894736842106),\n",
              " (tensor(-0.3015), 'Crow: City of Angels, The (1996)', 1.9487179487179487),\n",
              " (tensor(-0.2862), 'Barb Wire (1996)', 1.9333333333333333),\n",
              " (tensor(-0.2854),\n",
              "  'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
              "  1.7142857142857142),\n",
              " (tensor(-0.2795), 'Cable Guy, The (1996)', 2.339622641509434),\n",
              " (tensor(-0.2684), 'Mortal Kombat: Annihilation (1997)', 1.9534883720930232),\n",
              " (tensor(-0.2602), 'Free Willy 3: The Rescue (1997)', 1.7407407407407407),\n",
              " (tensor(-0.2561), 'Thinner (1996)', 2.4489795918367347),\n",
              " (tensor(-0.2510), 'Jungle2Jungle (1997)', 2.4393939393939394),\n",
              " (tensor(-0.2506), 'Keys to Tulsa (1997)', 2.24),\n",
              " (tensor(-0.2497), 'Island of Dr. Moreau, The (1996)', 2.1578947368421053),\n",
              " (tensor(-0.2425), 'Grease 2 (1982)', 2.0),\n",
              " (tensor(-0.2372), \"McHale's Navy (1997)\", 2.1884057971014492),\n",
              " (tensor(-0.2318), 'Beautician and the Beast, The (1997)', 2.313953488372093),\n",
              " (tensor(-0.2293),\n",
              "  \"City Slickers II: The Legend of Curly's Gold (1994)\",\n",
              "  2.5454545454545454)]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyZduCwf5KGf"
      },
      "source": [
        "Or the ones with the best bias:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "la5TBDHx5KGg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "feca7841-b560-4da7-f0fa-eb02a165ebdc"
      },
      "source": [
        "sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.5779), 'Shawshank Redemption, The (1994)', 4.445229681978798),\n",
              " (tensor(0.5761), \"Schindler's List (1993)\", 4.466442953020135),\n",
              " (tensor(0.5692), 'Titanic (1997)', 4.2457142857142856),\n",
              " (tensor(0.5392), 'Star Wars (1977)', 4.3584905660377355),\n",
              " (tensor(0.5322), 'L.A. Confidential (1997)', 4.161616161616162),\n",
              " (tensor(0.5235), 'Silence of the Lambs, The (1991)', 4.28974358974359),\n",
              " (tensor(0.4996), 'As Good As It Gets (1997)', 4.196428571428571),\n",
              " (tensor(0.4950), 'Rear Window (1954)', 4.3875598086124405),\n",
              " (tensor(0.4880), 'Vertigo (1958)', 4.251396648044692),\n",
              " (tensor(0.4853), 'Usual Suspects, The (1995)', 4.385767790262173),\n",
              " (tensor(0.4760), 'Apt Pupil (1998)', 4.1),\n",
              " (tensor(0.4690), 'Good Will Hunting (1997)', 4.262626262626263),\n",
              " (tensor(0.4678), \"One Flew Over the Cuckoo's Nest (1975)\", 4.291666666666667),\n",
              " (tensor(0.4470), 'Boot, Das (1981)', 4.203980099502488),\n",
              " (tensor(0.4376), 'Close Shave, A (1995)', 4.491071428571429)]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6evHscZk5KGh"
      },
      "source": [
        "There is certainly a strong correlation!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eVUbKjL5KGi"
      },
      "source": [
        "### Movie weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfpKAgSC5KGi"
      },
      "source": [
        "Now let's try to analyze the latent factors our model has learned. We can grab the weights for each movie in `top_movies` the same way as we did for the bias before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deDn2gcH5KGj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb85dfd5-cbd0-4071-8e74-5ce8608db53f"
      },
      "source": [
        "movie_w = learn.model.weight(top_movies, is_item=True)\n",
        "movie_w.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 50])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMKR-0Um5KGj"
      },
      "source": [
        "Let's try a PCA to reduce the dimensions and see if we can see what the model learned:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eaYCuVt5KGk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b86eff50-9ad4-4a02-ccac-544b9e37cf8d"
      },
      "source": [
        "movie_pca = movie_w.pca(3)\n",
        "movie_pca.shape"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gP8YQMM55KGl"
      },
      "source": [
        "fac0,fac1,fac2 = movie_pca.t()\n",
        "movie_comp = [(f, i) for f,i in zip(fac0, top_movies)]"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRscjh3K5KGl"
      },
      "source": [
        "Here are the highest score on the first dimension:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L74WWxjA5KGl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c6c8100-66a5-4332-8c49-c934ea348592"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(1.2272), 'D3: The Mighty Ducks (1996)'),\n",
              " (tensor(1.1664), 'Children of the Corn: The Gathering (1996)'),\n",
              " (tensor(1.1040), 'Free Willy 3: The Rescue (1997)'),\n",
              " (tensor(1.1024), 'Bio-Dome (1996)'),\n",
              " (tensor(1.0999), 'Leave It to Beaver (1997)'),\n",
              " (tensor(1.0959), 'Home Alone 3 (1997)'),\n",
              " (tensor(1.0824), 'Richie Rich (1994)'),\n",
              " (tensor(1.0695), \"McHale's Navy (1997)\"),\n",
              " (tensor(1.0662), 'Congo (1995)'),\n",
              " (tensor(1.0584), 'Batman & Robin (1997)')]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMS1ASOm5KGm"
      },
      "source": [
        "And the worst:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDxgTWUW5KGm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cdc7f55-b78e-4e5b-cf1e-428e382c0cb6"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-1.1930), 'Chinatown (1974)'),\n",
              " (tensor(-1.1030), 'Close Shave, A (1995)'),\n",
              " (tensor(-1.0767), 'Casablanca (1942)'),\n",
              " (tensor(-1.0451), \"One Flew Over the Cuckoo's Nest (1975)\"),\n",
              " (tensor(-1.0340), 'Lawrence of Arabia (1962)'),\n",
              " (tensor(-1.0295), 'When We Were Kings (1996)'),\n",
              " (tensor(-1.0036), 'Wrong Trousers, The (1993)'),\n",
              " (tensor(-1.0034), 'Shall We Dance? (1996)'),\n",
              " (tensor(-1.0021), 'Third Man, The (1949)'),\n",
              " (tensor(-0.9957), 'Godfather, The (1972)')]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXZyCaJL5KGn"
      },
      "source": [
        "Same thing for our second dimension:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JR6BnTn5KGn"
      },
      "source": [
        "movie_comp = [(f, i) for f,i in zip(fac1, top_movies)]"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xj4_P5dS5KGn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19b057dd-d264-46c4-a01a-2dee335aefe1"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(1.1277), 'Titanic (1997)'),\n",
              " (tensor(1.1249), 'Braveheart (1995)'),\n",
              " (tensor(0.9579), 'Raiders of the Lost Ark (1981)'),\n",
              " (tensor(0.9546), \"It's a Wonderful Life (1946)\"),\n",
              " (tensor(0.9516), 'Independence Day (ID4) (1996)'),\n",
              " (tensor(0.9231), 'American President, The (1995)'),\n",
              " (tensor(0.9216), 'Forrest Gump (1994)'),\n",
              " (tensor(0.8850), 'Pretty Woman (1990)'),\n",
              " (tensor(0.8701), 'Field of Dreams (1989)'),\n",
              " (tensor(0.8503), 'Shawshank Redemption, The (1994)')]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28K9s9Rp5KGo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de73d76d-3f65-4f01-af63-fa3ae33ab43e"
      },
      "source": [
        "sorted(movie_comp, key=itemgetter(0))[:10]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(-0.9964), 'Stuart Saves His Family (1995)'),\n",
              " (tensor(-0.9567), 'Trainspotting (1996)'),\n",
              " (tensor(-0.9436), 'Jude (1996)'),\n",
              " (tensor(-0.8676), 'Serial Mom (1994)'),\n",
              " (tensor(-0.8632), 'Sweet Hereafter, The (1997)'),\n",
              " (tensor(-0.8559), 'Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)'),\n",
              " (tensor(-0.8455), 'To Die For (1995)'),\n",
              " (tensor(-0.8355), 'Ready to Wear (Pret-A-Porter) (1994)'),\n",
              " (tensor(-0.7986), 'Brazil (1985)'),\n",
              " (tensor(-0.7525), 'Clockwork Orange, A (1971)')]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZAynsWP5KGo"
      },
      "source": [
        "And we can even plot the movies according to their scores on those dimensions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHtORCAp5KGp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "outputId": "5c9a5211-28f5-40c6-94c0-2b5ca9ce1eac"
      },
      "source": [
        "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
        "idxs = list(range(50))\n",
        "X = fac0[idxs]\n",
        "Y = fac2[idxs]\n",
        "plt.figure(figsize=(15,15))\n",
        "plt.scatter(X, Y)\n",
        "for i, x, y in zip(top_movies[idxs], X, Y):\n",
        "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
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    }
  ]
}